Bibliographic record
Abstract
OBJECTIVE: To appraise guidelines on cardiovascular risk assessment to guide selection of screening interventions for a health check. DATA SOURCES: Guidelines in the English language published between January 1, 2003, and May 2, 2009, were retrieved using MEDLINE and CINAHL. This was supplemented by searching the National Guideline Clearinghouse, National Library for Health, Canadian Medical Association Infobase, and G-I-N International Guideline Library. STUDY SELECTION: We included guidelines developed on behalf of professional organizations from Western countries, containing recommendations on cardiovascular risk assessment for the apparently healthy population. Titles and abstracts were assessed by 2 independent reviewers. Of 1984 titles identified, 27 guidelines met our criteria. DATA EXTRACTION: Rigor of guideline development was assessed by 2 independent reviewers. One reviewer extracted information on conflicts of interest and recommendations. RESULTS: Sixteen of 27 guidelines reported conflicts of interest and 17 showed considerable rigor. These included recommendations on assessment of total cardiovascular risk (7 guidelines), dyslipidemia (2), hypertension (2), and dysglycemia (7). Recommendations on total cardiovascular risk and dyslipidemia included prediction models integrating multiple risk factors, whereas remaining recommendations were focused on single risk factors. No consensus was found on recommended target populations, treatment thresholds, and screening tests. CONCLUSIONS: Differences among the guidelines imply important variation in allocation of preventive interventions. To make informed decisions, physicians should use only the recommendations from rigorously developed guidelines.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.028 | 0.168 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.021 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".